Practicing Machine Learning Interview Questions in Python

Loved by learners at thousands of top companies:

Course Description

Have you ever wondered how to properly prepare for a Machine Learning Interview? Of course you have or you likely wouldn't be reading this right now! In this course, students will prepare to answer 15 common Machine Learning (ML) interview questions for a data scientist role in Python. These questions will revolve around 7 important topics: data preprocessing, data visualization, supervised learning, unsupervised learning, model ensembling, model selection, and model evaluation. By the end of the course, the students will possess both the required theoretical background and the ability to develop Python code to successfully answer these 15 questions. The coding examples will be mainly based on the scikit-learn package given its ease-of-use and ability to cover the most important ML techniques in the Python language.

The course does not teach machine learning fundamentals as these are covered in the course's prerequisites.

Practicing Machine Learning Interview Questions in Python

In the first chapter of this course, you'll perform all the preprocessing steps required to create a predictive machine learning model, including what to do with missing values, outliers, and how to normalize your dataset.

In the third chapter of this course, you'll use unsupervised learning to apply feature extraction and visualization techniques for dimensionality reduction and clustering methods to select not only an appropriate clustering algorithm but optimal cluster number for a dataset.

In the second chapter of this course, you'll practice different several aspects of supervised machine learning techniques, such as selecting the optimal feature subset, regularization to avoid model overfitting, feature engineering, and ensemble models to address the so-called bias-variance trade-off.

In the fourth and final chapter of this course, you'll really step it up and apply bootstrapping and cross-validation to evaluate performance for model generalization, resampling techniques to imbalanced classes, detect and remove multicollinearity, and build an ensemble model.

1
Data Pre-processing and Visualization
Free

In the first chapter of this course, you'll perform all the preprocessing steps required to create a predictive machine learning model, including what to do with missing values, outliers, and how to normalize your dataset.
View Chapter Details Play Chapter Now
Supervised Learning

In the second chapter of this course, you'll practice different several aspects of supervised machine learning techniques, such as selecting the optimal feature subset, regularization to avoid model overfitting, feature engineering, and ensemble models to address the so-called bias-variance trade-off.
View Chapter Details Play Chapter Now
Unsupervised Learning

In the third chapter of this course, you'll use unsupervised learning to apply feature extraction and visualization techniques for dimensionality reduction and clustering methods to select not only an appropriate clustering algorithm but optimal cluster number for a dataset.
View Chapter Details Play Chapter Now
Model Selection and Evaluation

In the fourth and final chapter of this course, you'll really step it up and apply bootstrapping and cross-validation to evaluate performance for model generalization, resampling techniques to imbalanced classes, detect and remove multicollinearity, and build an ensemble model.
View Chapter Details Play Chapter Now

What do other learners have to say?

“I've used other sites, but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyd's Banking Group

“DataCamp is the top resource I recommend for learning data science.”

Louis Maiden

Harvard Business School

“DataCamp is by far my favorite website to learn from.”

Ronald Bowers

Decision Science Analytics @ USAA

Lisa Stuart

Data Scientist

Lisa Stuart is a Data Scientist with a wealth of industry experience. She is currently on the DSP Big Data Analytics Team at Amazon where she and her team use statistical analysis and machine learning to improve processes around successful and on-time delivery for each and every Amazon order. Prior to that, she built predictive models for targeted marketing at Costco and Expedia and managed dashboards for process automation. At Starbucks, she managed a team of data scientists to build a predictive model on geopolitical stability of countries around the world to make informed decisions on expansion and supply routes. In her free time, you'll find her at the dog park hanging out with her beloved German Shepherd/Husky Blaze.

If you type... We will search for...

Press enter to see all search results

Courses

Tracks

Instructors

Learning Experience

Business Features

Academic Features

Practicing Machine Learning Interview Questions in Python

Create Your Free Account

Loved by learners at thousands of top companies:

Course Description

Data Pre-processing and Visualization

Handling missing data

The hunt for missing values

Simple imputation

Iterative imputation

Data distributions and transformations

Training vs test set distributions and transformations

Train/test distributions

Log and power transformations

Data outliers and scaling

Outlier detection

Handling outliers

Z-score standardization

Unsupervised Learning

Dimensionality reduction: feature extraction

The curse of dimensionality

Principal component analysis

Singular value decomposition

Dimensionality reduction: visualization techniques

Reducing high-dimensional data

Visualization separation of classes with PCA I

Visualization PCs with a scree plot

Clustering analysis: selecting the right clustering algorithm

Clustering algorithms

K-means clustering

Hierarchical agglomerative clustering

Clustering analysis: choosing the optimal number of clusters

What is the optimal k?

Silhouette method

Elbow method

Supervised Learning

Regression: feature selection

Best feature subset

Filter and wrapper methods

Feature selection through feature importance

Regression: regularization

Avoiding overfitting

Lasso regularization

Ridge regularization

Classification: feature engineering

Classification model features

Logistic regression baseline classifier

Ensemble methods

Bootstrap aggregation (bagging)

Boosting

XG Boost

Model Selection and Evaluation

Model generalization: bootstrapping and cross-validation

Validating model performance

Decision tree

A forest of decision trees

Model evaluation: imbalanced classification models

X-ray weapon detection

Imbalanced class metrics

Resampling techniques

Model selection: regression models

Addressing multicollinearity

Multicollinearity techniques - feature engineering

Multicollinearity techniques - PCA

Model selection: ensemble models

Random forest vs gradient boosting

Random forest ensemble

Gradient boosting ensemble

Wrap-Up

Data Pre-processing and Visualization

Handling missing data

The hunt for missing values

Simple imputation